Developing a Census Based Generative Geodemographic Classification System

开发基于人口普查的生成地理人口分类系统

• 批准号: ES/Z50273X/1
• 负责人: Alex Singleton
• 金额: $ 42.59万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FZ50273X%2F1
• 关键词: Developing; Census; Based; Generative; Geodemographic

Leveraging the power of contemporary Artificial Intelligence (AI), this project aims to revolutionize the way in which we can build and use geodemographic classifications. This will do so by enabling more accurate representations of socio-spatial structure and lowering barriers to census based classification development. It also proposes a user-friendly online tool that will allow anyone to easily create their own tailored, research-ready census-based geodemographic data product.Geodemographic classifications provide useful and policy-relevant representations of the complex and multidimensional characteristics of populations living within small geographic areas. Classifications have been created using components of census data since the 1970s, with notable examples in 2001, 2011 and 2021 when the ONS co-produced the first open geodemographic classifications for the UK with academic partners. These "Output Area Classifications" (OAC) have garnered wide use and inspired localised models for specific geographic areas such as London (LOAC).The core methods used to build geodemographic classification have however remained reasonably static since the 1970s, with only modest update. Furthermore, the creation of classifications also remains a reasonably technical process, limiting the ability for others to produce their own classifications, either for localities or specific purposes.This proposal argues that recent developments in AI, and specifically deep learning and machine learning, show great potential to radically transform the power and utility of geodemographic classification. Firstly, through the creation of more accurate representations of socio-spatial structure; and, secondly, through improved geodemographic information systems that significantly reduce barriers to developing new classificationsAims and ObjectivesThe aim of this project is to update the established methods used to build Census based geodemographic classifications through the integration of AI into:The more automated development of output area level input measures that better account for non-linear geographic relationships between variables.A tool to that enables the automated description of clusters.Enabling the creation of a new public facing and online geodemographic classification system that will enable custom census-based classifications to be created.This will be achieved through the following objectives:Evaluating the use of autoencoders as a new method of data reduction for output area level geodemographic input measures.Developing an operational machine learning pipeline that takes output area level census inputs through to cluster creation.Utilising a large language model (LLM: such as integrated into ChatGPT), to develop an automated geodemographic descriptive tool capable of producing accurate textual descriptions of cluster characteristics.Producing a public facing online tool and accompanying training that will guide users to create their own research-ready census-based geodemographic data products.

该项目旨在利用当代人工智能 (AI) 的力量，彻底改变我们构建和使用地理人口统计分类的方式。这将通过更准确地表示社会空间结构并降低基于人口普查的分类开发的障碍来实现。它还提出了一个用户友好的在线工具，允许任何人轻松创建自己定制的、基于研究的人口普查地理人口统计数据产品。地理人口分类为生活在小范围内的人口的复杂和多维特征提供了有用且与政策相关的表示。地理区域。自 20 世纪 70 年代以来，人们一直使用人口普查数据的组成部分来创建分类，其中著名的例子是 2001 年、2011 年和 2021 年，ONS 与学术合作伙伴共同为英国制作了第一个开放的地理人口统计分类。这些“输出区域分类”(OAC) 已获得广泛使用，并激发了伦敦 (LOAC) 等特定地理区域的本地化模型。然而，自 20 世纪 70 年代以来，用于构建地理人口统计分类的核心方法一直保持相当静态，仅进行了适度更新。此外，分类的创建仍然是一个合理的技术过程，限制了其他人为地区或特定目的生成自己的分类的能力。该提案认为，人工智能的最新发展，特别是深度学习和机器学习，显示出巨大的潜力。具有从根本上改变地理人口分类的力量和效用的潜力。首先，通过创建更准确的社会空间结构表示；其次，通过改进地理人口统计信息系统，显着减少开发新分类的障碍目的和目标该项目的目的是通过将人工智能集成到以下内容中，更新用于构建基于人口普查的地理人口统计分类的既定方法：输出区域的更加自动化的开发更好地考虑变量之间非线性地理关系的水平输入测量。一种能够自动描述集群的工具。能够创建一个新的面向公众的在线地理人口统计分类系统，该系统将支持基于自定义人口普查的这将通过以下目标来实现：评估自动编码器作为输出区域级地理人口统计输入测量数据缩减的新方法的使用。开发可操作的机器学习管道，将输出区域级人口普查输入进行聚类利用大型语言模型（LLM：例如集成到 ChatGPT 中）开发一种自动化的地理人口描述工具，能够生成集群特征的准确文本描述。制作面向公众的在线工具和随附的培训将指导用户创建自己的、可用于研究的、基于人口普查的地理人口统计数据产品。